With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

In recent years, there have been extensive efforts in secondary schools to prevent, treat and raise awareness of adolescent mental health problems. For some adolescents, these efforts are essential and will lead to a reduction in clinical symptoms. However, it is also vital to assess whether, for others, the current approach might be causing iatrogenic harm. A growing body of quantitative research indicates that some aspects of school-based mental health interventions increase distress or clinical symptoms, relative to control activities, and qualitative work indicates that this may be partly due to the interventions themselves.

This report is based on the extrapolation to 2020 of data on the economic burden of mental illnesses in Pakistan in 2006. Given the resultant estimated high economic burden of mental illness in the country (£2.97 billion in 2020), we advocate a revised budget allocation to mental healthcare. As a resource-scarce nation that is entangled in natural disasters, Pakistan needs cost-effective psychological interventions such as culturally adapted manual-assisted problem-solving training (C-MAP) for the prevention of self-harm and suicide and to move towards attaining the United Nations’ Sustainable Development Goals (SDGs). Although government has taken initiatives to support healthcare services (such as the Sehat Sahulat Program for universal health coverage), there is still a need to implement a cost-effective national digital model for mental healthcare such as the Agha Khan Development Network Digital Health Programme.

Clozapine dose assessment in treatment-refractory schizophrenia is complicated. There is a narrow margin between an effective and a potentially toxic dose and wide inter-individual variation in clozapine metabolic capacity. Moreover, factors such as changes in smoking habit, infection/inflammation, co-prescription of certain drugs, notably fluvoxamine, and age alter the dose requirement within individuals. Therapeutic drug monitoring (TDM) of plasma clozapine and N-desmethylclozapine (norclozapine) can help assess adherence, guide dosage and guard against toxicity. This article gives an overview of clozapine pharmacokinetics and factors affecting clozapine dose requirements. It then outlines the procedures and processes of clozapine TDM, from taking the blood sample for laboratory assay or point-of-contact (finger-prick) testing (POCT) to interpreting and acting on the results.